湖南大学学报（自然科学版）2024，Vol.51Issue(12)：78-86,9.DOI:10.16339/j.cnki.hdxbzkb.2024285
基于MDS-YOLO模型的小目标检测问题研究
Research of Small Object Detection Problem Based on MDS-YOLO Model
摘要
Abstract
To solve the problem of large computation and low accuracy of the current mainstream algorithms for small object detection,this paper replaces the backbone network in YOLOv4 with the lightweight network MobileNetV3,and replaces some ordinary convolutions in the neck network with depthwise separable convolutions.At the same time,a new loss function IF-EIoU Loss is defined for small object detection.Therefore,MDS-YOLO object detection model is constructed.This model has a high detection speed and good detection performance for small object.To verify the effectiveness of the model,experiments are carried out on MS COCO dataset and Visdrone2019 dataset,respectively.Compared with the YOLOv4 algorithm,on MS COCO dataset,the average detection accuracy of the MDS-YOLO algorithm is improved by 1.5 percentage points,the detection accuracy of small object is increased by 3.3 percentage points,and the detection speed is also increased from 31 frames per second to 36 frames per second.On the Visdrone2019 dataset,the MDS-YOLO algorithm increases the average detection accuracy from 14.9%of YOLOv4 to 16.3%.The experimental results show that the MDS-YOLO algorithm proposed can effectively improve the detection accuracy of small object.关键词
小目标检测/YOLOv4算法/轻量级网络MobileNetV3/IF-EIoU Loss/MS COCO数据集Key words
small object detection/YOLOv4 algorithm/lightweight network MobileNetV3/IF-EIoU Loss/MS COCO dataset分类
数理科学引用本文复制引用
朱恩文,梁曌,肖进文,梁小林..基于MDS-YOLO模型的小目标检测问题研究[J].湖南大学学报（自然科学版）,2024,51(12):78-86,9.基金项目
国家自然科学基金重点资助项目(51839002,52338009),National Natural Science Foundation of China(51839002,52338009) （51839002,52338009）
国家杰出青年科学基金资助项目(52025085),National Science Fund for Distinguished Young(52025085) （52025085）
湖南省自然科学基金资助项目(2021JJ30734),Natural Science Foundation of Hunan Province(2021JJ30734) （2021JJ30734）
湖南省研究生创新性课题(CX20220952),Hunan Provincial Innovation Foundation for Postgraduate(CX20220952) （CX20220952）
